How to Learn Machine Learning

Machine learning (ML) is a type of artificial intelligence (AI) that lets software get better at predicting what will happen without being told to do so.  Machine learning algorithms use data from the past as input to make predictions about what the future will be like.

What is Machine Learning?

Different Types of Machine Leaning??

Supervised  Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Why Learn Machine Learning

When you learn about machine learning, you open up a world of possibilities for   creating cutting-edge applications in fields like cybersecurity, image recognition, medicine, face recognition and move humanity forward.

Let's Now See  Steps to Learn Machine Learning 😎

Learn Prerequisites

Step 1

Learn programming language Python

Learn basic Statistics and Maths

Learn to analyse DataSets

Learn Linear Algebra

Step 2

Good understanding of linear algebra is essential for understanding and working with many ML algorithms, especially deep learning algorithms

Learn Core Machine Learning Algorithms

Step 3

- Gradient Descent - Basic Linear Regression - Clustering Algorithms - Decision Tree - Naive Bayes Algorithm

Learn Using Python Libraries

Step 4

- Learn Numpy - Learn Pandas - Learn Scikit-Learn

Learn to Code Machine Learning Models

Step 4

In this step, you need to learn using Scikit-Learn python library to train and test Machine Learning Models

Learn to deploy Machine Learning Model

Step 5

So if you have trained a Machine Learning Model but now to make it available for people to use, you need to either use Docker, Kubernetes or Pytorch 

Final Words

That's it

Machine Learning is a quite difficult field to succeed in, because you really need to learn a lot of stuff even to get started.  But do keep in mind that its worth it because salaries for ML Engineers is way higher as compared to regular software engineering jobs.